Eye movements are extremely fast and precise and remain largely intact in several neurological and medical conditions causing generalized weakness and inability to speak and write. Such relatively common conditions include strokes, amyotrophic lateral sclerosis (Lou Gehrig's disease), inflammatory polyneuropathies and intubation due to critical illness, etc.
Communication with eye movements alone could also be valuable for virtual reality, gaming, marketing and in various environments where speaking is not possible or desirable.
Several eye-tracking solutions have been proposed. Some of them typically require the use of dedicated hardware such as infrared cameras, thereby reducing their availability and increasing the cost of such technology. For example, eye tracking systems designed for paralyzed individuals are so expensive that they are unaffordable for most patients and clinical units.
A few experimental systems using ambient light only and ordinary webcams exist but their performance is poor when lighting conditions are degraded (dim/side lighting) or when the facial features of the user are not strictly controlled (face not perpendicular to the camera, light eye color, dark skin color, etc.).
Capturing eye movements with an ordinary mobile device camera may be challenging because of elements such as the many degrees of freedom of such systems including parallel movements of the head and eyes, the changing lighting conditions, the variability in eye and face shape and color, the sampling rate limitations of ordinary mobile device cameras, and/or the processor speed limitations of mobile devices.
Therefore, there is a need for an improved eye tracking method and system that may operate under ambient light conditions.